Discriminative learning using linguistic features to rescore n-best speech hypotheses

Maria Georgescul, Manny Rayner, P. Bouillon, Nikos Tsourakis
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引用次数: 4

Abstract

We describe how we were able to improve the accuracy of a medium-vocabulary spoken dialog system by rescoring the list of n-best recognition hypotheses using a combination of acoustic, syntactic, semantic and discourse information. The non-acoustic features are extracted from different intermediate processing results produced by the natural language processing module, and automatically filtered. We apply discriminative support vector learning designed for re-ranking, using both word error rate and semantic error rate as ranking target value, and evaluating using five-fold cross-validation; to show robustness of our method, confidence intervals for word and semantic error rates are computed via bootstrap sampling. The reduction in semantic error rate, from 19% to 11%, is statistically significant at 0.01 level.
基于语言特征的判别学习对n个最佳语音假设进行评分
我们描述了我们如何能够通过使用声学、句法、语义和话语信息的组合来重新记录n个最佳识别假设列表,从而提高中等词汇量口语对话系统的准确性。从自然语言处理模块产生的不同中间处理结果中提取非声学特征,并进行自动过滤。我们采用了为重新排序设计的判别性支持向量学习,使用单词错误率和语义错误率作为排序目标值,并使用五倍交叉验证进行评估;为了显示我们的方法的鲁棒性,单词和语义错误率的置信区间是通过自举抽样计算的。语义错误率从19%降低到11%,在0.01水平上具有统计学意义。
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